How to Scope Your MVP When Building With AI
Technology
•
May 2, 2025
Scoping a minimum viable product (MVP) has always been both an art and a science. With the rise of AI—especially Large Language Models—the rules have changed, but the stakes are even higher. Getting your AI-powered MVP right means you get to market faster, learn what works, and avoid burning cash on features no one uses. But how do you actually scope an MVP when you’re building with AI?
The first thing to understand is that an AI MVP isn’t just about proving your tech works—it’s about validating that AI adds unique value to your users. According to McKinsey’s 2024 report on AI startups, the most successful launches were the ones that focused on a single, high-value use case and got it into users’ hands quickly. Think of how Jasper started as a simple AI copywriter, or how Zapier launched its AI-based automation features with just a handful of key integrations before expanding. These companies didn’t try to build everything at once; they picked the one job that mattered most and did it well.
Start by mapping the workflow you want to transform. What are your users doing today that is slow, tedious, or error-prone? That’s your starting point. For example, if you’re building an AI tool to automate customer support, your MVP might focus on handling only the most common FAQs—rather than aiming to replace human agents entirely. Klarna’s AI chat, which now automates two-thirds of support requests, started by only answering billing questions, as detailed in their 2024 product case study.
The next step is to clarify which part of the workflow actually needs AI. Not every feature benefits from a model; sometimes a simple rule or script is enough. LLMs and AI should be reserved for the points where human-like understanding, summarization, or creativity are needed. According to Y Combinator’s guidance for AI startups, teams that try to “AI everything” often end up slower, not faster. Instead, scope your MVP around a clear, testable moment of value—then use no-code tools, manual steps, or off-the-shelf solutions for the rest.
Finally, you’ll want to bake in real-world feedback from the start. Don’t build a black box and hope for the best. Use tools like Loom, FullStory, or even regular Zoom calls to watch early users interact with your AI. The best MVPs are launched with tight, fast feedback loops—GitHub, for example, improved Copilot by releasing early and iterating on user suggestions in real time. In fact, Andreessen Horowitz’s 2024 survey found that the top-performing AI MVPs launched within three months and iterated weekly with user data.
The bottom line? Building an AI MVP isn’t about chasing every possible feature—it’s about finding one point of real leverage, proving it fast, and learning from every customer interaction. Start small, validate early, and be relentless about what goes into your scope. In the world of AI products, focus and speed are your biggest competitive advantages.
References:
Related insights
How to Scope Your MVP When Building With AI
Technology
•
May 2, 2025
Scoping a minimum viable product (MVP) has always been both an art and a science. With the rise of AI—especially Large Language Models—the rules have changed, but the stakes are even higher. Getting your AI-powered MVP right means you get to market faster, learn what works, and avoid burning cash on features no one uses. But how do you actually scope an MVP when you’re building with AI?
The first thing to understand is that an AI MVP isn’t just about proving your tech works—it’s about validating that AI adds unique value to your users. According to McKinsey’s 2024 report on AI startups, the most successful launches were the ones that focused on a single, high-value use case and got it into users’ hands quickly. Think of how Jasper started as a simple AI copywriter, or how Zapier launched its AI-based automation features with just a handful of key integrations before expanding. These companies didn’t try to build everything at once; they picked the one job that mattered most and did it well.
Start by mapping the workflow you want to transform. What are your users doing today that is slow, tedious, or error-prone? That’s your starting point. For example, if you’re building an AI tool to automate customer support, your MVP might focus on handling only the most common FAQs—rather than aiming to replace human agents entirely. Klarna’s AI chat, which now automates two-thirds of support requests, started by only answering billing questions, as detailed in their 2024 product case study.
The next step is to clarify which part of the workflow actually needs AI. Not every feature benefits from a model; sometimes a simple rule or script is enough. LLMs and AI should be reserved for the points where human-like understanding, summarization, or creativity are needed. According to Y Combinator’s guidance for AI startups, teams that try to “AI everything” often end up slower, not faster. Instead, scope your MVP around a clear, testable moment of value—then use no-code tools, manual steps, or off-the-shelf solutions for the rest.
Finally, you’ll want to bake in real-world feedback from the start. Don’t build a black box and hope for the best. Use tools like Loom, FullStory, or even regular Zoom calls to watch early users interact with your AI. The best MVPs are launched with tight, fast feedback loops—GitHub, for example, improved Copilot by releasing early and iterating on user suggestions in real time. In fact, Andreessen Horowitz’s 2024 survey found that the top-performing AI MVPs launched within three months and iterated weekly with user data.
The bottom line? Building an AI MVP isn’t about chasing every possible feature—it’s about finding one point of real leverage, proving it fast, and learning from every customer interaction. Start small, validate early, and be relentless about what goes into your scope. In the world of AI products, focus and speed are your biggest competitive advantages.
References: